Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …

Extensive efforts to gather materials data have largely overlooked potential data
redundancy. In this study, we present evidence of a significant degree of redundancy across …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

In many real-world applications, graph-structured data used for training and testing have
differences in distribution, such as in high energy physics (HEP) where simulation data used …

In real-world materials research, machine learning (ML) models are usually expected to
predict and discover novel exceptional materials that deviate from the known materials. It is …

Amid the global call for decarbonization efforts, uranium and lithium are two important metal
resources critical for securing a sustainable energy future. Extraction of uranium and lithium …

Water pollution poses a significant environmental threat due to the discharge of organic
dyes from industrial processes. In this study, we investigated a novel adsorptive composite …

Scientific machine learning (ML) aims to develop generalizable models, yet assessments of
generalizability often rely on heuristics. Here, we demonstrate in the materials science …

Growing materials data and data-driven informatics drastically promote the discovery and
design of materials. While there are significant advancements in data-driven models, the …